Journal
SPECTROCHIMICA ACTA PART A-MOLECULAR AND BIOMOLECULAR SPECTROSCOPY
Volume 246, Issue -, Pages -Publisher
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.saa.2020.118986
Keywords
Variable selection; Multivariate calibration; Intelligent optimization algorithm; Particle swarm optimization; Near-infrared spectroscopy
Categories
Funding
- Anhui Provincial Key Research and Development Program [201904c03020007]
- Strategic Priority Research Program of the Chinese Academy of Sciences [XDA08040107]
- National Natural Science Foundation of China [32070399]
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A new wavelength selection algorithm based on CMW and VDPSO algorithms is proposed in this study, with VDPSO-CMW showing better performance in experiments.
We propose a new wavelength selection algorithm based on combined moving window(CMW) and variable dimension particle swarm optimization (VDPSO) algorithm. CMW retains the advantages of the moving window algorithm, and different windows can overlap each other to realize automatic optimization of spectral interval width and number. VDPSO algorithms improve the PSO algorithm. They can search the data space in different dimensions, and reduce the risk of limited local extrema and over fitting. Four different high-performance variable selection algorithms-BOSS, VCPA, iVISSA and IRF-are compared in three NIR data sets (corn, beer and fuel). The results show that VDPSO-CMW has better performance. The Matlab codes for implementing PSO-CWM and VDPSO-CMW are freely available on the website: https://www.mathworks.com/matlabcentral/fileexchange/ 75828-a-variable-selection-method. (C) 2020 Elsevier B.V. All rights reserved.
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